Multiscale Entropy Analysis of Complex Physiologic Time Series

TLDR

Quantifying complexity of physiological time series has attracted interest, yet traditional algorithms paradoxically assign higher complexity to pathological random outputs than to healthy long-range correlated dynamics, likely because they ignore multiple inherent time scales. The study introduces a method to compute multiscale entropy for complex time series. The authors develop a multiscale entropy calculation that evaluates signal complexity across multiple temporal scales. MSE robustly separates healthy and pathological groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.

Abstract

There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.